Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, dissertation, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

DEEP LEARNING ALGORITHM IMPLEMENTATION FOR SHIP DETECTION IN SPOT SATELLITE IMAGES

HANIZAM, MOHD HAZIQ NAZMI (2019) DEEP LEARNING ALGORITHM IMPLEMENTATION FOR SHIP DETECTION IN SPOT SATELLITE IMAGES. IRC, Universiti Teknologi PETRONAS. (Submitted)

[img] PDF
Restricted to Registered users only

Download (2660Kb)

Abstract

Marine industry is a large industry especially in the economy sector. Not limited to commercial, this industry also includes naval sector and the small and medium industry of fisheries all over the world. The huge development throughout the industry has also develop many kinds of unlawful act such as piracy and illegal cargo transportation. This has become the call for action for the officials of the sovereignty area to monitor the activities to control the situation and prevent them from become an epidemic that effects the whole industry. In this study, we propose to implement a deep-learning approach for detection of ships on satellite images in various conditions. The deep-learning algorithm to be deployed is Faster R-CNN and to be implemented using MATLAB. The project is carried out with the objective to implement the algorithm on SPOT satellite images that can accurately localize the region of interest (ROI) of the ship. The implementation of the algorithm consists of three stages which are pre-processing, network training and accuracy evaluation. The output of this project will be the localization of ships within the image with confidence scores of the prediction. Based on the results obtained, the deployment of the Faster R-CNN algorithm on ship class objects from SPOT satellite images has achieved a noteworthy performance despite the limitations in the amount of training dataset and specifications of the machines used. We can conclude that the project was able to achieve its objectives within the stipulated timeframe.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Microelectronics - Sensor Development
Subject: UNSPECIFIED
Divisions: Engineering > Electrical and Electronic
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 20 Dec 2019 16:14
Last Modified: 20 Dec 2019 16:14
URI: http://utpedia.utp.edu.my/id/eprint/20131

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...